Latency-Aware Network Slicing for 5G URLLC Applications: Design and Optimization Strategies

The growing need for 5G URLLC applications has led to the development of new network slicing techniques that are focused on very low latency and ultra-high reliability. This paper proposes network slicing with the collaboration of dynamic Resource Controller (ReCon) based on machine learning, which delivers full performance in diverse scenarios like smart city for autonomous driving, telemedicine remote surgery or industrial grade automation. Compared to state-of-the-art solutions, the proposed method yields large latency reductions and increased robustness in speed as well as additional improvements of reliability. By adjusting to changes in the network and efficiently handling connections between devices, this strategy is also positioning us well for growth: as usage increases and new applications are brought on board. Analysis and simulations cover what the approach can do for 5G network performance, showing it as a benchmark in network optimization and add further on with next-generation of Network Slicing advancements.